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  • Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning
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Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning

Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning

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Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning

Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning

1 (3)

Overview of this book

Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem.
Table of Contents (8 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
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Free Chapter
1
Introducing Machine Learning and ML-Agents
Introducing Machine Learning and ML-Agents
Machine Learning
ML-Agents
Running a sample
Creating an environment
Academy, Agent, and Brain
Summary
2
The Bandit and Reinforcement Learning
The Bandit and Reinforcement Learning
Reinforcement Learning
Contextual bandits and state
Exploration and exploitation
MDP and the Bellman equation
Q-Learning and connected agents
Exercises
Summary
3
Deep Reinforcement Learning with Python
Deep Reinforcement Learning with Python
Installing Python and tools
ML-Agents external brains
Neural network foundations
Deep Q-learning
Proximal policy optimization
Exercises
Summary
4
Going Deeper with Deep Learning
Going Deeper with Deep Learning
Agent training problems
Convolutional neural networks
Experience replay
Partial observability, memory, and recurrent networks
Asynchronous actor – critic training
Exercises
Summary
5
Playing the Game
Playing the Game
Multi-agent environments
Adversarial self-play
Decisions and On-Demand Decision Making
Imitation learning
Curriculum Learning
Exercises
Summary
6
Terrarium Revisited – A Multi-Agent Ecosystem
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Terrarium Revisited – A Multi-Agent Ecosystem
What was/is Terrarium?
Building the Agent ecosystem
Basic Terrarium – Plants and Herbivores
Carnivore: the hunter
Next steps
Exercises
Summary
7
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Exercises

Try to complete at least one of the following exercises on your own. Using the skills you just learned only reinforces your learning, and you should really appreciate that concept by now. Do an exercise; your brain will thank you later:

  1. Create a variety of different plant species, each with different points. If you are very ambitious, build different prefabs and use other models to represent different species.
  2. Create a variety of different herbivore species, each with different points and perhaps different models. Do your new animals perform better or worse?
  1. Create a variety of different carnivore species again with different points and other models. Feel free to download other free poly models on your own and use those.
  2. Encourage your friends or colleagues to build agents and see who can build and train the best creature. You can share this by exporting your creature...
End of Section 7
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